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Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas

Puyol-Antón E, Ruijsink B, Langet H, De Craene M, Piro P, Schnabel JA, King AP (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Verlag

Book Volume: 11395 LNCS

Pages Range: 94-102

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Granada, ESP

ISBN: 9783030120283

DOI: 10.1007/978-3-030-12029-0_11

Abstract

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank ((Formula Presented) 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.

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How to cite

APA:

Puyol-Antón, E., Ruijsink, B., Langet, H., De Craene, M., Piro, P., Schnabel, J.A., & King, A.P. (2019). Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas. In Maxime Sermesant, Jichao Zhao, Alistair Young, Kawal Rhode, Mihaela Pop, Kristin McLeod, Tommaso Mansi, Shuo Li (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 94-102). Granada, ESP: Springer Verlag.

MLA:

Puyol-Antón, Esther, et al. "Learning Associations Between Clinical Information and Motion-Based Descriptors Using a Large Scale MR-derived Cardiac Motion Atlas." Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Maxime Sermesant, Jichao Zhao, Alistair Young, Kawal Rhode, Mihaela Pop, Kristin McLeod, Tommaso Mansi, Shuo Li, Springer Verlag, 2019. 94-102.

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